def plot_psycurve_fit_and_data(bdata, plotColor):
#Calculate the psychometric
rightTrials = bdata['choice'] == bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues, fractionHitsEachValue, ciHitsEachValue, nTrialsEachValue,
nHitsEachValue) = behavioranalysis.calculate_psychometric(
rightTrials, freqEachTrial, valid)
#Calculate the psycurve fit
estimate = behavioranalysis.psycurve_fit_from_bdata(bdata, plotFits=0)
#Plot in log2 space(fit is already in this space)
possibleValues = np.log2(possibleValues)
#Plot the stuff
ax = plt.gca()
plt.hold(1)
xRange = possibleValues[-1] - possibleValues[1]
fitxval = np.linspace(possibleValues[0] - 0.1 * xRange,
possibleValues[-1] + 0.1 * xRange, 40)
fityvals = extrastats.psychfun(fitxval, *estimate)
upperWhisker = ciHitsEachValue[1, :] - fractionHitsEachValue
lowerWhisker = fractionHitsEachValue - ciHitsEachValue[0, :]
(pline, pcaps, pbars) = plt.errorbar(possibleValues,
fractionHitsEachValue,
yerr=[lowerWhisker, upperWhisker],
color='k',
fmt=None)
pdots = plt.plot(possibleValues,
fractionHitsEachValue,
'o',
mec='none',
mfc='k',
ms=8)
setp(pcaps, color=plotColor)
setp(pbars, color=plotColor)
setp(pdots, markerfacecolor=plotColor)
if np.unique(freqEachTrial)[0] < 1000: #If Hz
ax.set_xticks(possibleValues)
ax.set_xticklabels(np.unique(freqEachTrial))
plt.xlabel('Frequency (Hz)')
else:
ax.set_xticks(possibleValues)
freqLabels = [
'{:.03}'.format(x) for x in np.unique(freqEachTrial) / 1000.0
]
ax.set_xticklabels(freqLabels)
plt.xlabel('Frequency (kHz)')
ax.plot(fitxval, fityvals, color=plotColor)
plt.ylim([0, 1])
plt.ylabel('Fraction Rightward Trials')
return estimate
def plot_psycurve_fit_and_data_nohigh(bdata, plotFits=True):
'''
This version actually works. Calculates things in log2(freq) space.
'''
rightTrials = bdata['choice'] == bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues, fractionHitsEachValue, ciHitsEachValue, nTrialsEachValue,
nHitsEachValue) = behavioranalysis.calculate_psychometric(
rightTrials, freqEachTrial, valid)
plot(np.log2(possibleValues), fractionHitsEachValue, 'bo')
hold(1)
possibleFreq = np.log2(np.unique(freqEachTrial))
lowerFreqConstraint = possibleFreq[1]
upperFreqConstraint = possibleFreq[-2]
maxFreq = max(possibleFreq)
minFreq = min(possibleFreq)
constraints = ('Uniform({}, {})'.format(lowerFreqConstraint,
upperFreqConstraint),
'Uniform(0,5)', 'Uniform(0,1)', 'Uniform(0,1)')
estimate = extrastats.psychometric_fit(possibleFreq[:-1],
nTrialsEachValue[:-1],
nHitsEachValue[:-1], constraints)
xRange = possibleFreq[-1] - possibleFreq[1]
fitxval = np.linspace(possibleFreq[0] - 0.1 * xRange,
possibleFreq[-1] + 0.1 * xRange, 40)
fityvals = extrastats.psychfun(fitxval, *estimate)
plot(fitxval, fityvals)
ylim([0, 1])
return estimate
def plot_frequency_psycurve_soundtype(bdata, soundType, fontsize=12):
'''
Show psychometric curve (for any arbitrary sound type)
'''
trialsSoundType = bdata['soundType']==bdata.labels['soundType'][soundType]
choice = bdata['choice']
choiceRight = choice==bdata.labels['choice']['right']
choiceRightSoundType = choiceRight[trialsSoundType]
targetFrequencySoundType = bdata['targetFrequency'][trialsSoundType]
valid = bdata['valid']
validSoundType = valid[trialsSoundType]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRightSoundType,targetFrequencySoundType,validSoundType)
(pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(1e-3*possibleValues,
fractionHitsEachValue,
ciHitsEachValue,
xTickPeriod=1)
plt.xlabel('Frequency (kHz)',fontsize=fontsize)
plt.ylabel('Rightward trials (%)',fontsize=fontsize)
extraplots.set_ticks_fontsize(plt.gca(),fontsize)
return (pline, pcaps, pbars, pdots)
def plot_psycurve_fit_and_data(bdata, plotFits=True):
rightTrials = bdata['choice'] == bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues, fractionHitsEachValue, ciHitsEachValue, nTrialsEachValue,
nHitsEachValue) = behavioranalysis.calculate_psychometric(
rightTrials, freqEachTrial, valid)
#Calculate the lower and upper freq in the bdata to set the correct constraints
# possibleFreq = np.unique(freqEachTrial)
possibleFreq = np.log2(np.unique(freqEachTrial))
maxFreq = max(possibleFreq)
minFreq = min(possibleFreq)
#Calculating with constraints
# constraints = ( 'Uniform({}, {})'.format(minFreq, maxFreq),'Uniform(0,20)' ,'Uniform(0,1)', 'Uniform(0,1)')
constraints = ('Uniform({}, {})'.format(minFreq, maxFreq), 'Uniform(0,10)',
'Uniform(0,0.2)', 'Uniform(0,0.2)')
estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue,
nHitsEachValue, constraints)
if plotFits:
xRange = possibleFreq[-1] - possibleFreq[1]
fitxval = np.linspace(possibleFreq[0] - 0.1 * xRange,
possibleFreq[-1] + 0.1 * xRange, 40)
#The fitted points
fityvals = extrastats.psychfun(fitxval, estimate[0], estimate[1],
estimate[2], estimate[3])
plot(fitxval, fityvals)
# hold(1)
# extraplots.plot_psychometric(possibleFreq, fractionHitsEachValue, ciHitsEachValue)
plot(np.log2(possibleFreq), fractionHitsEachValue, 'bo')
return estimate
salineSessions = ['20160412a', '20160414a', '20160416a']
animal = 'amod002'
muscimolData = behavioranalysis.load_many_sessions(animal, muscimolSessions)
salineData = behavioranalysis.load_many_sessions(animal, salineSessions)
soundTypes = ['amp_mod', 'chords']
soundType = 'chords'
trialsSoundTypeMusAmp = muscimolData['soundType']==muscimolData.labels['soundType'][soundType]
rightTrialsMusAmp = (muscimolData['choice']==muscimolData.labels['choice']['right'])[trialsSoundTypeMusAmp]
freqEachTrialMusAmp = muscimolData['targetFrequency'][trialsSoundTypeMusAmp]
validMusAmp = muscimolData['valid'][trialsSoundTypeMusAmp]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrialsMusAmp, freqEachTrialMusAmp, validMusAmp)
M = pypsignifit.psignidata.BootstrapInference(zip(possibleValues, nHitsEachValue, nTrialsEachValue), sample=True, nafc=1, plotprm={"color": "r", "label": "Condition 0"})
trialsSoundTypeSalAmp = salineData['soundType']==salineData.labels['soundType'][soundType]
rightTrialsSalAmp = (salineData['choice']==salineData.labels['choice']['right'])[trialsSoundTypeSalAmp]
freqEachTrialSalAmp = salineData['targetFrequency'][trialsSoundTypeSalAmp]
validSalAmp = salineData['valid'][trialsSoundTypeSalAmp]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrialsSalAmp, freqEachTrialSalAmp, validSalAmp)
S = pypsignifit.psignidata.BootstrapInference(zip(possibleValues, nHitsEachValue, nTrialsEachValue), sample=True, nafc=1, plotprm={"color": "k", "label": "Condition 0"})
figure()
# pypsignifit.psigniplot.plotPMF(B)
salineSessions = ['20160412a', '20160414a', '20160416a']
animal = 'amod002'
muscimolData = behavioranalysis.load_many_sessions(animal, muscimolSessions)
salineData = behavioranalysis.load_many_sessions(animal, salineSessions)
soundTypes = ['amp_mod', 'chords']
soundType = 'chords'
trialsSoundTypeMusAmp = muscimolData['soundType']==muscimolData.labels['soundType'][soundType]
rightTrialsMusAmp = (muscimolData['choice']==muscimolData.labels['choice']['right'])[trialsSoundTypeMusAmp]
freqEachTrialMusAmp = muscimolData['targetFrequency'][trialsSoundTypeMusAmp]
validMusAmp = muscimolData['valid'][trialsSoundTypeMusAmp]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrialsMusAmp, freqEachTrialMusAmp, validMusAmp)
M = pypsignifit.psignidata.BootstrapInference(zip(possibleValues, nHitsEachValue, nTrialsEachValue), sample=True, nafc=1, plotprm={"color": "r", "label": "Condition 0"})
trialsSoundTypeSalAmp = salineData['soundType']==salineData.labels['soundType'][soundType]
rightTrialsSalAmp = (salineData['choice']==salineData.labels['choice']['right'])[trialsSoundTypeSalAmp]
freqEachTrialSalAmp = salineData['targetFrequency'][trialsSoundTypeSalAmp]
validSalAmp = salineData['valid'][trialsSoundTypeSalAmp]
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrialsSalAmp, freqEachTrialSalAmp, validSalAmp)
S = pypsignifit.psignidata.BootstrapInference(zip(possibleValues, nHitsEachValue, nTrialsEachValue), sample=True, nafc=1, plotprm={"color": "k", "label": "Condition 0"})
figure()
# pypsignifit.psigniplot.plotPMF(B)
muscimolData = behavioranalysis.load_many_sessions(animal, muscimolSessions)
salineData = behavioranalysis.load_many_sessions(animal, salineSessions)
soundTypes = ['amp_mod', 'chords']
soundType = 'chords'
trialsSoundTypeMusAmp = muscimolData['soundType'] == muscimolData.labels[
'soundType'][soundType]
rightTrialsMusAmp = (muscimolData['choice'] == muscimolData.labels['choice']
['right'])[trialsSoundTypeMusAmp]
freqEachTrialMusAmp = muscimolData['targetFrequency'][trialsSoundTypeMusAmp]
validMusAmp = muscimolData['valid'][trialsSoundTypeMusAmp]
(possibleValues, fractionHitsEachValue, ciHitsEachValue, nTrialsEachValue,
nHitsEachValue) = behavioranalysis.calculate_psychometric(
rightTrialsMusAmp, freqEachTrialMusAmp, validMusAmp)
M = pypsignifit.psignidata.BootstrapInference(zip(possibleValues,
nHitsEachValue,
nTrialsEachValue),
sample=True,
nafc=1,
plotprm={
"color": "r",
"label": "Condition 0"
})
trialsSoundTypeSalAmp = salineData['soundType'] == salineData.labels[
'soundType'][soundType]
rightTrialsSalAmp = (salineData['choice'] == salineData.labels['choice']
['right'])[trialsSoundTypeSalAmp]
animal, muscimolSessions)
salineDataObjs, salineSoundTypes = behavioranalysis.load_behavior_sessions_sound_type(
animal, salineSessions)
#Testing with a single bdata object
hold(1)
bdata = salineDataObjs[0]
bdata = muscimolDataObjs[0]
#Calculate hit trials, freq each trial, valid - then calc psychometric
rightTrials = bdata['choice'] == bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues, fractionHitsEachValue, ciHitsEachValue, nTrialsEachValue,
nHitsEachValue) = behavioranalysis.calculate_psychometric(
rightTrials, freqEachTrial, valid)
extraplots.plot_psychometric(possibleValues, fractionHitsEachValue,
ciHitsEachValue)
#calculating the estimate without constraints
estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue,
nHitsEachValue)
#Calculating with constraints
constraints = ('Uniform(8, 32)', 'Uniform(0,20)', 'Uniform(0,1)',
'Uniform(0,1)')
estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue,
nHitsEachValue, constraints)
#The x values over which to evaluate the fitted function
def plot_psycurve_fit_and_data_nohigh(bdata, plotFits=True):
'''
This version actually works. Calculates things in log2(freq) space.
'''
rightTrials = bdata['choice']==bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrials, freqEachTrial, valid)
plot(np.log2(possibleValues), fractionHitsEachValue, 'bo')
hold(1)
possibleFreq = np.log2(np.unique(freqEachTrial))
lowerFreqConstraint = possibleFreq[1]
upperFreqConstraint = possibleFreq[-2]
maxFreq = max(possibleFreq)
minFreq = min(possibleFreq)
constraints = ( 'Uniform({}, {})'.format(lowerFreqConstraint, upperFreqConstraint), 'Uniform(0,5)' ,'Uniform(0,1)', 'Uniform(0,1)')
estimate = extrastats.psychometric_fit(possibleFreq[:-1], nTrialsEachValue[:-1], nHitsEachValue[:-1], constraints)
xRange = possibleFreq[-1]-possibleFreq[1]
fitxval = np.linspace(possibleFreq[0]-0.1*xRange,possibleFreq[-1]+0.1*xRange,40)
fityvals = extrastats.psychfun(fitxval, *estimate)
plot(fitxval, fityvals)
ylim([0, 1])
return estimate
def plot_psycurve_fit_and_data(bdata, plotFits=True):
rightTrials = bdata['choice']==bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrials, freqEachTrial, valid)
#Calculate the lower and upper freq in the bdata to set the correct constraints
# possibleFreq = np.unique(freqEachTrial)
possibleFreq = np.log2(np.unique(freqEachTrial))
maxFreq = max(possibleFreq)
minFreq = min(possibleFreq)
#Calculating with constraints
# constraints = ( 'Uniform({}, {})'.format(minFreq, maxFreq),'Uniform(0,20)' ,'Uniform(0,1)', 'Uniform(0,1)')
constraints = ( 'Uniform({}, {})'.format(minFreq, maxFreq), 'Uniform(0,10)' ,'Uniform(0,0.2)', 'Uniform(0,0.2)')
estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue, nHitsEachValue, constraints)
if plotFits:
xRange = possibleFreq[-1]-possibleFreq[1]
fitxval = np.linspace(possibleFreq[0]-0.1*xRange,possibleFreq[-1]+0.1*xRange,40)
#The fitted points
fityvals = extrastats.psychfun(fitxval, estimate[0], estimate[1], estimate[2], estimate[3])
plot(fitxval, fityvals)
# hold(1)
# extraplots.plot_psychometric(possibleFreq, fractionHitsEachValue, ciHitsEachValue)
plot(np.log2(possibleFreq), fractionHitsEachValue, 'bo')
return estimate
#Load the data objects for the muscimol and saline sessions muscimolDataObjs, muscimolSoundTypes = behavioranalysis.load_behavior_sessions_sound_type(animal, muscimolSessions) salineDataObjs, salineSoundTypes = behavioranalysis.load_behavior_sessions_sound_type(animal, salineSessions) #Testing with a single bdata object hold(1) bdata = salineDataObjs[0] bdata = muscimolDataObjs[0] #Calculate hit trials, freq each trial, valid - then calc psychometric rightTrials = bdata['choice']==bdata.labels['choice']['right'] freqEachTrial = bdata['targetFrequency'] valid = bdata['valid'] (possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrials, freqEachTrial, valid) extraplots.plot_psychometric(possibleValues, fractionHitsEachValue, ciHitsEachValue) #calculating the estimate without constraints estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue, nHitsEachValue) #Calculating with constraints constraints = ( 'Uniform(8, 32)','Uniform(0,20)' ,'Uniform(0,1)', 'Uniform(0,1)') estimate = extrastats.psychometric_fit(possibleValues, nTrialsEachValue, nHitsEachValue, constraints) #The x values over which to evaluate the fitted function xRange = possibleValues[-1]-possibleValues[1] fitxval = np.linspace(possibleValues[0]-0.1*xRange,possibleValues[-1]+0.1*xRange,40) #The fitted points
choiceRight = choice==bdata.labels['choice']['right']
choiceRightAmpMod = choiceRight[trialsAmpMod]
choiceRightTones = choiceRight[trialsTones]
targetFrequencyAmpMod = bdata['targetFrequency'][trialsAmpMod]
targetFrequencyTones = bdata['targetFrequency'][trialsTones]
valid = bdata['valid']
validAmpMod = valid[trialsAmpMod]
validTones = valid[trialsTones]
#Tones
figure()
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRightTones,targetFrequencyTones,validTones)
(pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(1e-3*possibleValues,
fractionHitsEachValue,
ciHitsEachValue,
xTickPeriod=1)
#Amp Mod
figure()
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue)=\
behavioranalysis.calculate_psychometric(choiceRightAmpMod,targetFrequencyAmpMod,validAmpMod)
(pline, pcaps, pbars, pdots) = extraplots.plot_psychometric(1e-3*possibleValues,
fractionHitsEachValue,
def plot_psycurve_fit_and_data(bdata, plotColor):
#Calculate the psychometric
rightTrials = bdata['choice']==bdata.labels['choice']['right']
freqEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(rightTrials, freqEachTrial, valid)
#Calculate the psycurve fit
estimate = behavioranalysis.psycurve_fit_from_bdata(bdata, plotFits=0)
#Plot in log2 space(fit is already in this space)
possibleValues = np.log2(possibleValues)
#Plot the stuff
ax = plt.gca()
plt.hold(1)
xRange = possibleValues[-1]-possibleValues[1]
fitxval = np.linspace(possibleValues[0]-0.1*xRange,possibleValues[-1]+0.1*xRange,40)
fityvals = extrastats.psychfun(fitxval, *estimate)
upperWhisker = ciHitsEachValue[1,:]-fractionHitsEachValue
lowerWhisker = fractionHitsEachValue-ciHitsEachValue[0,:]
(pline, pcaps, pbars) = plt.errorbar(possibleValues, fractionHitsEachValue,
yerr = [lowerWhisker, upperWhisker],color='k', fmt=None)
pdots = plt.plot(possibleValues, fractionHitsEachValue, 'o',mec='none',mfc='k',ms=8)
setp(pcaps, color=plotColor)
setp(pbars, color=plotColor)
setp(pdots, markerfacecolor=plotColor)
if np.unique(freqEachTrial)[0]<1000: #If Hz
ax.set_xticks(possibleValues)
ax.set_xticklabels(np.unique(freqEachTrial))
plt.xlabel('Frequency (Hz)')
else:
ax.set_xticks(possibleValues)
freqLabels = ['{:.03}'.format(x) for x in np.unique(freqEachTrial)/1000.0]
ax.set_xticklabels(freqLabels)
plt.xlabel('Frequency (kHz)')
ax.plot(fitxval, fityvals, color=plotColor)
plt.ylim([0, 1])
plt.ylabel('Fraction Rightward Trials')
return estimate